The profound, pervasive and enduring consequences of ageing population present enormous challenges as well as enormous opportunities for Information and Communication Technology. The EU funded OASIS project, a Large Scale Integrated Project, is aimed to develop an open and innovative reference architecture, based upon ontologies and semantic services, that will allow plug and play and cost-effective interconnection of existing and new services in all domains required for the independent and autonomous living of the elderly and their enhanced quality of life. Among other technological advances, in OASIS we are developing a smart multisensorial platform for monitoring the lower limbs movements, as well as the muscular activations. We are using unobtrusive integrated sensors to transduce posture and kinematic variables and to acquire surface Electromiography (sEMG). The platform is able to analyze and merge the sEMG signals and kinematics variables to provide a single coherent dynamic information of the acquired movements. A Predictive Dynamic Model (PDM) based on machine learning techniques assesses the physiological muscular recruitments as well as muscular fatigue and physiological conditions.
An Ontology-driven Multisensorial Platform to Enable Unobtrusive Human Monitoring and Independent Living
Ricci G.;SICILIANO, GABRIELE;DE ROSSI, DANILO EMILIO
2009-01-01
Abstract
The profound, pervasive and enduring consequences of ageing population present enormous challenges as well as enormous opportunities for Information and Communication Technology. The EU funded OASIS project, a Large Scale Integrated Project, is aimed to develop an open and innovative reference architecture, based upon ontologies and semantic services, that will allow plug and play and cost-effective interconnection of existing and new services in all domains required for the independent and autonomous living of the elderly and their enhanced quality of life. Among other technological advances, in OASIS we are developing a smart multisensorial platform for monitoring the lower limbs movements, as well as the muscular activations. We are using unobtrusive integrated sensors to transduce posture and kinematic variables and to acquire surface Electromiography (sEMG). The platform is able to analyze and merge the sEMG signals and kinematics variables to provide a single coherent dynamic information of the acquired movements. A Predictive Dynamic Model (PDM) based on machine learning techniques assesses the physiological muscular recruitments as well as muscular fatigue and physiological conditions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
